Generated Knowledge Prompting
Reasoning
Level 2 · Advanced reasoningAsk the model to generate relevant background knowledge first, then use it to answer — instead of answering directly from an unstated assumption of what it knows.
Generated Knowledge Prompting asks the model to first produce a short list of facts or background knowledge relevant to the question, and only then answer using that knowledge as an explicit foundation. Making the model's implicit knowledge explicit before it reasons over it tends to produce more accurate, better-grounded answers than jumping straight to a response.
When to use it
Use it for questions that depend on background knowledge not stated in the question itself — general knowledge questions, questions requiring domain context the model has to recall before reasoning.
When to avoid it
Avoid it for questions that are fully self-contained — generating facts adds nothing there.
Advantages
- Surfaces the model's relevant background knowledge explicitly, instead of leaving it implicit inside the reasoning
- Makes it possible to spot a wrong or missing fact before it silently corrupts the final answer
- Complements Grounded prompting well: one generates knowledge, the other restricts to provided knowledge
Disadvantages
- The generated facts can themselves be wrong — this doesn't guarantee accuracy, only makes errors more visible
- Adds length compared to a direct answer
Examples
Why do some countries drive on the left side of the road? Before answering, generate 3-5 relevant facts or pieces of background knowledge about the history of road traffic conventions. Then use that knowledge to answer the question.
Pairs well with
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- Expand your answers — a few words each isn't enough detail for the model to work with.
- Add background context so the model understands the situation.